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New GRIP system challenges graph-based search for learned sparse retrieval

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new graph-based approximate nearest neighbor (ANN) system for learned sparse retrieval (LSR) called GRIP. This system challenges the prevailing notion that inverted indexes are superior to graph-based indexes for LSR. GRIP incorporates techniques such as term-based entry point seeding, approximate routing via sparse sketching, and sparse partition-based graph construction to enhance performance. On the MS MARCO v2 benchmark, GRIP demonstrated a 2.5x latency improvement over Seismic, a leading inverted index for LSR, while maintaining 95% recall@10. AI

IMPACT This research could shift the standard approach for deploying learned sparse retrieval systems, potentially leading to more efficient search technologies.

RANK_REASON The cluster contains a research paper detailing a new system and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New GRIP system challenges graph-based search for learned sparse retrieval

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The cluster contains a research paper detailing a new system and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Rossano Venturini ·

    Getting a GRIP on Graph-Based Search for Learned Sparse Retrieval

    Learned sparse retrieval (LSR) has recently gained traction in the information retrieval community for the ability to combine the generalization capabilities of neural representations with the interpretability of keyword-based search. A number of recent works have demonstrated th…

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